Trust-based context-aware mobile social network service recommendation

Xu, Jun; Zhong, Yuansheng; Zhu, Wenwu; Sun, Fei

doi:10.1007/s11859-017-1228-3

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…40 Renren: This dataset is Chinese social networking service similar to Facebook in which the users are allowed to connect and communicate with each other, share information and access mobile live streaming. 41 INFOCOM: This dataset contains users' connection via their contacts and attributes. 42 Douban: This data set is a social networking website in which registered users can record information such as ratings related to recent events, books, music and activities and share the review to their friends.…”

Section: Evaluation Datasetsmentioning

confidence: 99%

“…Renren: This dataset is Chinese social networking service similar to Facebook in which the users are allowed to connect and communicate with each other, share information and access mobile live streaming 41 …”

Section: Main Concepts and Associated Terminologiesmentioning

confidence: 99%

“…Utilizing the trust factor, Xu et al 41 presented a recommendation algorithm for appropriate services recommendation in mobile social networks. In their algorithm, the more trustworthy neighbors of the target users were determined according to the users' similarity degree and the friends' familiarity level.…”

Section: Primary Studies' Categorization Schemesmentioning

confidence: 99%

See 2 more Smart Citations

Social networks data analytical approaches for trust‐based recommender systems: A systematic literature review

Vatani,

Rahmani,

Javadi

et al. 2023

Int J Communication

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SummaryWith the explosive growth of information on the web and the quick provision of novel web services, recommendation systems have emerged as efficient mechanism for providing advice regarding items in which users might be potentially interested. However, a traditional recommender system, which solely mines the user's previous behavior and item descriptions for recommendations, has some drawbacks. To overcome these limitations, a new solution that has recently garnered significant attention is using trust data. This approach, however, presents challenges in utilizing suitable trust data according to recommender systems applications, underlying social network structures, and user needs. In addition, in a selective decision‐making system, trust, as a kind of social network data, plays a significant role and needs an appropriate approach for making inferences. This article provides a systematic literature review of the current trust‐based social recommender systems. It also presents a detailed categorization of the trust type utilized and inferred from the existing trust‐related social networks data analytical approaches. Furthermore, it addresses the main properties and challenges of the most popular trust‐based social recommendation systems. Finally, it presents our findings and discusses open issues that provide researchers with insight to develop more enhanced recommender systems.

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Section: Evaluation Datasetsmentioning

confidence: 99%

Section: Main Concepts and Associated Terminologiesmentioning

confidence: 99%

See 1 more Smart Citation

Social networks data analytical approaches for trust‐based recommender systems: A systematic literature review

Vatani,

Rahmani,

Javadi

et al. 2023

Int J Communication

View full text Add to dashboard Cite

show abstract

“…This experiment uses Root Mean Squared Error (RMSE) as a measure of the accuracy of predictive rating [24]. For each user u and item i in the test set, let T denote the rating set of user u for item i, r ui is the actual rating user u for item i and the predicted score given by the recommendation algorithm, and then RMSE is defined as Equation (8).…”

Section: Experiments Evaluation Indicatorsmentioning

confidence: 99%

“…Time and space overhead are large. Xu et al proposed a method of integrating the trust relationship of social network friends, effectively alleviating the sparseness of data [8]. In order to reduce the sparsity of the matrix, Jing et al used the singular value decomposition (SVD) method to reduce the dimension of the scoring matrix to lower the sparseness of the matrix [9], but the computation of this method is large.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm based on Spark

Tao¹,

Gan²,

Wen³

2019

IJPE

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With the advent of the era of big data, the problem of information overload has become particularly serious. The recommendation system can provide personalized recommendation services for users by analyzing users' basic information and users' behavior information. How to push information accurately and efficiently has become an urgent issue in the era of big data. Based on the Alternating Least Squares (ALS) collaborative filtering recommendation algorithm, this paper reduces the loss of the invisible factor item attribute information by merging the similarity of the item on the loss function. At the same time, the cold start strategy is introduced into the model to complete the recommendation. The algorithm is implemented on the Spark distributed platform and single node by using the Movie Lens dataset published by the GroupLens Lab. The experiment results show that the proposed recommendation algorithm can preferably alleviate the data sparsity problem compared with the traditional recommendation algorithm. Moreover, the algorithm improves the accuracy of recommendation and the efficiency of calculation.

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